In order to improve the classification accuracy of fault data sets, the combination of ReliefF algorithm and Quantum Particle Swarm Optimization (QPSO) is adopted to propose a sensitive fault feature selection method that can reduce the dimension of fault data sets. First of all, the fault signal of after filtering de-noising multi-domain quantitative feature extraction, based on the setting time domain and frequency domain characteristics, the characteristics of each frequency band energy of wavelet packet decomposed as to describe the state of the rotor system fault initial fault feature set, and the typical fault of rotor system analog signal collection got a primitive failure data set. Then, the weight obtained through iterative calculation by the Relief F algorithm is used to weight each feature vector of the fault data set, and the threshold is set to eliminate the irrelevant features, so as to realize the first screening of each feature of the original fault data set. Finally, a quantum particle swarm optimization (QPSO) algorithm is introduced to filter feature sets twice, eliminate redundant features that are not conducive to classification, and optimize the parameters of support vector machines at the same time. By processing, a simplified optimal feature subset and the most appropriate set of support vector machines parameters are obtained. The method performance is verified by the original fault data set. The results show that this method can effectively screen out low-dimensional fault data sets with small size and high fault pattern recognition, which can significantly improve the identification accuracy of fault classifier.
薛瑞,赵荣珍. ReliefF与QPSO结合的故障特征选择算法[J]. 振动与冲击, 2020, 39(11): 171-176.
XUE Rui, ZHAO Rongzhen. The fault feature selection algorithm of combination of ReliefF and QPSO. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 171-176.
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